DDC function - RDocumentation (2024)

Arguments

X

X is the input data, and must be an \(n\) by \(d\) matrix or a data frame.

DDCpars

A list of available options:

• fracNA
  Only consider columns and rows with fewer NAs (missing values) than this fraction (percentage). Defaults to \(0.5\).

• numDiscrete
  A column that takes on numDiscrete or fewer values will be considered discrete and not used in the analysis. Defaults to \(3\).

• precScale
  Only consider columns whose scale is larger than precScale. Here scale is measured by the median absolute deviation. Defaults to \(1e-12\).

• cleanNAfirst
  If "columns", first columns then rows are checked for NAs. If "rows", first rows then columns are checked for NAs. "automatic" checks columns first if \(d \geq 5n\) and rows first otherwise. Defaults to "automatic".

• tolProb
  Tolerance probability, with default \(0.99\), which determines the cutoff values for flagging outliers in several steps of the algorithm.

• corrlim
  When trying to estimate \(z_{ij}\) from other variables \(h\), we will only use variables \(h\) with \(|\rho_{j,h}| \ge corrlim\). Variables \(j\) without any correlated variables \(h\) satisfying this are considered standalone, and treated on their own. Defaults to \(0.5\).

• combinRule
  The operation to combine estimates of \(z_{ij}\) coming from other variables \(h\): can be "mean", "median", "wmean" (weighted mean) or "wmedian" (weighted median). Defaults to wmean.

• returnBigXimp
  If TRUE, the imputed data matrix Ximp in the output will include the rows and columns that were not part of the analysis (and can still contain NAs). Defaults to FALSE.

• silent
  If TRUE, statements tracking the algorithm's progress will not be printed. Defaults to FALSE.

• nLocScale
  When estimating location or scale from more than nLocScale data values, the computation is based on a random sample of size nLocScale to save time. When nLocScale = 0 all values are used. Defaults to 25000.

• fastDDC
  Whether to use the fastDDC option or not. The fastDDC algorithm uses approximationsto allow to deal with high dimensions. Defaults to TRUE for \(d > 750\) and FALSE otherwise.

• standType
  The location and scale estimators used for robust standardization. Should be one of "1stepM", "mcd" or "wrap". See estLocScale for more info. Only used when fastDDC = FALSE. Defaults to "1stepM".

• corrType
  The correlation estimator used to find the neighboring variables. Must be one of "wrap" (wrapping correlation), "rank" (Spearman correlation) or "gkwls" (Gnanadesikan-Kettenring correlation followed by weighting). Only used when fastDDC = FALSE. Defaults to "gkwls".

• transFun
  The transformation function used to compute the robust correlations when fastDDC = TRUE. Can be "wrap" or "rank". Defaults to "wrap".

• nbngbrs
  When fastDDC = TRUE, each column is predicted from at most nbngbrs columns correlated to it. Defaults to 100.

Author

Raymaekers J., Rousseeuw P.J., Van den Bossche W.

Examples

Run this code

library(MASS); set.seed(12345)n <- 50; d <- 20A <- matrix(0.9, d, d); diag(A) = 1x <- mvrnorm(n, rep(0,d), A)x[sample(1:(n * d), 50, FALSE)] <- NAx[sample(1:(n * d), 50, FALSE)] <- 10x[sample(1:(n * d), 50, FALSE)] <- -10x <- cbind(1:n, x)DDCx <- DDC(x)cellMap(DDCx$stdResid)# For more examples, we refer to the vignette:if (FALSE) {vignette("DDC_examples")}

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